Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving

Understand

Predicting the behaviors of other road users is crucial to safe and intelligent decision-making for autonomous vehicles (AVs).

• However, most motion prediction models ignore the influence of the AV's actions and the planning module has to treat other agents as unalterable moving obstacles.
• To address this problem, this paper proposes an interaction-aware motion prediction model that is able to predict other agents' future trajectories according to the ego agent's future plan, i.e., their reactions to the ego's actions.
• Specifically, we employ Transformers to effectively encode the driving scene and incorporate the AV's plan in decoding the predicted trajectories.